{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Pandas第二课作业\n",
    "\n",
    "####  作业提交说明：\n",
    "- 位置：作业文件统一放置于/0.Teacher/Exercise/Pandas2/下\n",
    "- 文件名：请先复制该notebook文件，并重新命名为(课程名)+(您姓名的全拼)，并按要求完成后保存\n",
    "- 时间：课程结束后的第二天前提交。\n",
    "- 注意：请勿抄袭，移动，修改，删除其他同学和原始空白的练习文件。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "- 作业所需数据文件位于0.Teacher/data目录下\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### 1.读入0.Teacher/Data/下NVDA.csv中的数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Date        Open        High         Low       Close   Adj Close  \\\n",
      "0     1999-01-22    1.750000    1.953125    1.552083    1.640625    1.523430   \n",
      "1     1999-01-25    1.770833    1.833333    1.640625    1.812500    1.683028   \n",
      "2     1999-01-26    1.833333    1.869792    1.645833    1.671875    1.552448   \n",
      "3     1999-01-27    1.677083    1.718750    1.583333    1.666667    1.547611   \n",
      "4     1999-01-28    1.666667    1.677083    1.651042    1.661458    1.542776   \n",
      "5     1999-01-29    1.661458    1.666667    1.583333    1.583333    1.470231   \n",
      "6     1999-02-01    1.583333    1.625000    1.583333    1.614583    1.499249   \n",
      "7     1999-02-02    1.583333    1.625000    1.442708    1.489583    1.383178   \n",
      "8     1999-02-03    1.468750    1.541667    1.458333    1.520833    1.412196   \n",
      "9     1999-02-04    1.541667    1.645833    1.520833    1.604167    1.489577   \n",
      "10    1999-02-05    1.630208    1.666667    1.588542    1.651042    1.533103   \n",
      "11    1999-02-08    1.661458    1.666667    1.593750    1.593750    1.479904   \n",
      "12    1999-02-09    1.625000    1.635417    1.510417    1.531250    1.421868   \n",
      "13    1999-02-10    1.531250    1.572917    1.489583    1.515625    1.407360   \n",
      "14    1999-02-11    1.520833    1.708333    1.520833    1.645833    1.528267   \n",
      "15    1999-02-12    1.666667    1.750000    1.666667    1.739583    1.615320   \n",
      "16    1999-02-16    1.770833    1.843750    1.572917    1.750000    1.624992   \n",
      "17    1999-02-17    1.708333    1.729167    1.625000    1.656250    1.537939   \n",
      "18    1999-02-18    1.708333    1.729167    1.635417    1.682292    1.562121   \n",
      "19    1999-02-19    1.666667    1.770833    1.645833    1.739583    1.615320   \n",
      "20    1999-02-22    1.770833    1.791667    1.656250    1.750000    1.624992   \n",
      "21    1999-02-23    1.791667    1.869792    1.687500    1.833333    1.702373   \n",
      "22    1999-02-24    2.104167    2.187500    1.932292    1.979167    1.837789   \n",
      "23    1999-02-25    2.062500    2.125000    1.885417    1.916667    1.779754   \n",
      "24    1999-02-26    1.937500    2.000000    1.812500    1.828125    1.697537   \n",
      "25    1999-03-01    1.875000    1.916667    1.750000    1.838542    1.707209   \n",
      "26    1999-03-02    1.833333    1.843750    1.791667    1.822917    1.692701   \n",
      "27    1999-03-03    1.833333    1.833333    1.687500    1.697917    1.576630   \n",
      "28    1999-03-04    1.781250    1.791667    1.645833    1.661458    1.542776   \n",
      "29    1999-03-05    1.677083    1.760417    1.677083    1.755208    1.629829   \n",
      "...          ...         ...         ...         ...         ...         ...   \n",
      "4624  2017-06-08  153.460007  160.000000  151.789993  159.940002  159.940002   \n",
      "4625  2017-06-09  164.740005  168.500000  142.750000  149.600006  149.600006   \n",
      "4626  2017-06-12  145.880005  151.699997  142.110001  149.970001  149.970001   \n",
      "4627  2017-06-13  154.399994  154.770004  145.649994  151.399994  151.399994   \n",
      "4628  2017-06-14  151.520004  154.059998  148.500000  151.720001  151.720001   \n",
      "4629  2017-06-15  146.960007  153.600006  146.500000  152.369995  152.369995   \n",
      "4630  2017-06-16  152.759995  154.699997  150.240005  151.619995  151.619995   \n",
      "4631  2017-06-19  153.410004  157.529999  153.259995  157.320007  157.320007   \n",
      "4632  2017-06-20  159.029999  161.740005  156.919998  157.089996  157.089996   \n",
      "4633  2017-06-21  158.210007  159.619995  155.699997  159.470001  159.470001   \n",
      "4634  2017-06-22  159.800003  160.339996  157.399994  158.369995  158.369995   \n",
      "4635  2017-06-23  158.679993  159.320007  153.220001  153.830002  153.830002   \n",
      "4636  2017-06-26  155.160004  156.600006  148.330002  152.149994  152.149994   \n",
      "4637  2017-06-27  151.440002  151.789993  146.350006  146.580002  146.580002   \n",
      "4638  2017-06-28  149.320007  151.940002  145.750000  151.750000  151.750000   \n",
      "4639  2017-06-29  150.600006  150.720001  144.080002  146.679993  146.679993   \n",
      "4640  2017-06-30  147.380005  147.929993  143.500000  144.559998  144.559998   \n",
      "4641  2017-07-03  145.050003  145.649994  138.580002  139.330002  139.330002   \n",
      "4642  2017-07-05  141.899994  144.220001  141.130005  143.050003  143.050003   \n",
      "4643  2017-07-06  141.869995  145.380005  139.759995  143.479996  143.479996   \n",
      "4644  2017-07-07  145.779999  147.500000  144.850006  146.759995  146.759995   \n",
      "4645  2017-07-10  149.740005  154.000000  148.679993  153.699997  153.699997   \n",
      "4646  2017-07-11  153.850006  156.190002  152.149994  155.880005  155.880005   \n",
      "4647  2017-07-12  158.300003  163.000000  156.559998  162.509995  162.509995   \n",
      "4648  2017-07-13  163.000000  166.300003  158.750000  160.630005  160.630005   \n",
      "4649  2017-07-14  161.289993  165.009995  161.009995  164.949997  164.949997   \n",
      "4650  2017-07-17  166.330002  167.500000  161.750000  164.250000  164.250000   \n",
      "4651  2017-07-18  161.779999  166.550003  161.300003  165.960007  165.960007   \n",
      "4652  2017-07-19  166.330002  167.399994  164.610001  165.100006  165.100006   \n",
      "4653  2017-07-20  165.929993  167.509995  163.910004  167.500000  167.500000   \n",
      "\n",
      "        Volume  \n",
      "0     67867200  \n",
      "1     12762000  \n",
      "2      8580000  \n",
      "3      6109200  \n",
      "4      5688000  \n",
      "5      6100800  \n",
      "6      3867600  \n",
      "7      6602400  \n",
      "8      1878000  \n",
      "9      4548000  \n",
      "10     3421200  \n",
      "11     3852000  \n",
      "12     2174400  \n",
      "13     3705600  \n",
      "14     3306000  \n",
      "15     2743200  \n",
      "16     5275200  \n",
      "17     1693200  \n",
      "18     1767600  \n",
      "19     1884000  \n",
      "20     5131200  \n",
      "21     3452400  \n",
      "22    15319200  \n",
      "23     3728400  \n",
      "24     4315200  \n",
      "25     2304000  \n",
      "26     1381200  \n",
      "27     1534800  \n",
      "28     1434000  \n",
      "29     1969200  \n",
      "...        ...  \n",
      "4624  29043600  \n",
      "4625  92323200  \n",
      "4626  42438300  \n",
      "4627  41812600  \n",
      "4628  29616000  \n",
      "4629  24095600  \n",
      "4630  23124000  \n",
      "4631  19454400  \n",
      "4632  27386100  \n",
      "4633  17066300  \n",
      "4634  11728300  \n",
      "4635  27214700  \n",
      "4636  26599000  \n",
      "4637  24987300  \n",
      "4638  24873700  \n",
      "4639  26610600  \n",
      "4640  18203400  \n",
      "4641  17726800  \n",
      "4642  20504700  \n",
      "4643  18657100  \n",
      "4644  16374300  \n",
      "4645  23962300  \n",
      "4646  18948900  \n",
      "4647  28630200  \n",
      "4648  34228300  \n",
      "4649  23548700  \n",
      "4650  23269800  \n",
      "4651  19416000  \n",
      "4652  17176100  \n",
      "4653  17363300  \n",
      "\n",
      "[4654 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "cvs = pd.read_csv(\"Data/NVDA.csv\");\n",
    "\n",
    "print(cvs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.请计算nvda股票Adj Close每天的log return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.请找出nvda股票涨幅最大的10天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. 将这十天的股票信息都输出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5把英伟达每个月最后一天的股价记录下来，做成一张新的月线图，然后把Adj Close画成一张图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.使用concat方法把英伟达的Adj Close与其他三只股票拼接在一起"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.从Google有数据的那一天起开始画出四只股票的Adj Close折线图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
